1. Field of the Invention
The present invention relates to an abnormal pattern candidate detecting apparatus. More particularly, the present invention relates to an abnormal pattern candidate detecting apparatus that detects abnormal pattern candidates within an image, based on radiation image data of a subject.
2. Description of the Related Art
In the field of medicine, abnormal pattern candidate detecting processing systems (computer assisted image diagnosis apparatuses) have been proposed in, for example, U.S. Pat. No. 5,761,334. These systems enable objective and automatic detection of abnormal pattern candidates within radiation images of subjects by employing computers, without being affected by the experience or ability of a diagnostician.
These abnormal pattern candidate detecting processing systems detect mainly semispherical core regions as candidates for tumor patterns. The detection is performed based on characteristic density distributions and characteristic shapes of abnormal patterns, by utilizing iris filter processes and the like.
Meanwhile, tumor patterns that appear in radiation images are not limited to those in which cores can be visually recognized. For example, there are cases in which only radially extending linear structures called spicula, which exist at the peripheries of cores, can be seen in the images (refer to P1 of FIG. 1). There are cases in which tumors are made up of only spicula, without a core. There are also cases in which cores exist, yet are not pictured in the images. Particularly, as tumors having spicula are considered to have a high possibility of being malignant, it is an important objective to detect tumor patterns having spicula. Also, there is a high possibility that a tumor exists even if a core is not pictured, in portions such as: those in which distributions of breast tissue (mammary glands, blood vessels and the like) deviate from anatomical patterns, and are locally disarranged (referred to as “structural disturbance”, refer to Pa of FIG. 1); those in which tissue is locally drawn into one spot (refer to P2 of FIG. 1); and those in which tissue is drawn in over a large region (refer to P3 of FIG. 1).
However, conventional abnormal pattern candidate detecting processing systems employing iris filters detect core regions of tumors that are pictured within images, based on characteristics thereof, such as density distributions. Therefore, it is impossible in principle for these systems to detect tumor patterns, of which the cores cannot be visually recognized within the images, such as those described above.
Therefore, processes employing morphology filters have been proposed in Kobatake, Hidefumi: “Morphology”, K. K. Corona, 1996, pp. 161-165, and in Japanese Unexamined Patent Publication No. 2002-133397. The morphology filter processes extract linear structures from within images to detect tumor pattern candidates such as those described above.
Abnormal pattern candidate detecting apparatuses for extracting tumor pattern candidates having linear structures have also been proposed in, for example, Japanese Unexamined Patent Publication No. 2002-133397. In these abnormal pattern candidate detecting apparatuses, the degree of concentration of lines around a specific point is defined as a feature called “linear concentration”. Tumor patterns having linear structures are extracted based on the value of the linear concentration.
In addition, methods of extracting tumor pattern candidates having radially distributed linear structures have been proposed in, for example, Mekada, Yoshihito et al.: “Features of Local Concentration Patterns in Line Figures and Their Applications”, Journal of the Society of Electronic Data and Communications J77-D-II, 9, 1994, pp.1788-1796. These methods define a uniformity index that quantifies the uniformity of concentration of lines around a specific point. Tumor patterns having radially distributed linear structures are extracted based on the uniformity index. The uniformity index quantifies the uniformity of concentration by utilizing variance in linear concentration within each of a plurality of regions around a pixel of interest, divided into equiangular intervals. The uniformity index is calculated according to the following formula (1).U(M)=1−2√{square root over (var{CRi(M)})}  (1)
wherein:
U(M): uniformity index of a pixel of interest M;
i: a number for identifying each of N regions around the pixel of interest M, divided at equiangular intervals, iε[0, N];
Ri: each of the regions;
CRi (M): linear concentration within the N regions; and
var: variance of the linear concentration CRi (M) within the N regions.
The linear concentration only indicates the degree of concentration of lines around a specific point. Therefore, it is not possible to distinguish between a case in which lines converge from all directions, as shown in FIG. 2A, and a case in which lines converge from specific directions, as shown in FIG. 2B, by using the linear concentration. Accordingly, in the case that a mammogram (a diagnostic radiation image in which a breast is the subject) is the subject of the abnormal pattern candidate detecting apparatus disclosed in Japanese Unexamined Patent Publication No. 2002-133397, not only tumors having radially distributed spicula, but also mammary glands, in which lines are distributed unidirectionally from a point, will be detected. If detection criteria are made strict to avoid false positive detection results, a possibility arises that abnormal patterns made up of spicula will be overlooked.
On the other hand, the uniformity index indicates the uniformity of concentration of lines around a specific point toward the specific point. Therefore, it is possible to distinguish the difference among variances in the direction of concentration of lines around a specific point, as in FIG. 2A and FIG. 2B. However, there are cases in which the uniformity index cannot correctly express the variances in direction. This is because the uniformity index assumes its minimum value of 0 when the linear concentration of half of the divided regions is 1 and the linear concentration of the other half of the divided regions is 0, assumes its maximum value of 1 when the linear concentrations of all of the regions are equal, and has symmetric properties. For example, consider a case in which a donut shaped region K around a pixel of interest M is divided into eight regions at equiangular intervals of 45 degrees. In the case that the linear concentrations of two of the eight regions are 1 while the linear concentrations of the rest of the regions are 0, as shown in FIG. 3A, the uniformity index is 0.134. In the case that the linear concentrations of two of the eight regions are 0 while the linear concentrations of the rest of the regions are 1, as shown in FIG. 3B, the uniformity index is 0.134. Therefore, the variances in direction cannot be correctly quantified, regardless of the fact the variance is greater in the example shown in FIG. 3B.